class_docstr: Augmenter that simulate ocr error by random values. For example, OCR may recognize I as 1 incorrectly.        Pre-defined OCR mapping is leveraged to replace character by possible OCR error.
  
  :param float aug_char_p: Percentage of character (per token) will be augmented.
  :param int aug_char_min: Minimum number of character will be augmented.
  :param int aug_char_max: Maximum number of character will be augmented. If None is passed, number of augmentation is
      calculated via aup_char_p. If calculated result from aug_char_p is smaller than aug_char_max, will use calculated result
      from aup_char_p. Otherwise, using aug_max.
  :param float aug_word_p: Percentage of word will be augmented.
  :param int aug_word_min: Minimum number of word will be augmented.
  :param int aug_word_max: Maximum number of word will be augmented. If None is passed, number of augmentation is
      calculated via aup_word_p. If calculated result from aug_word_p is smaller than aug_word_max, will use calculated result
      from aug_word_p. Otherwise, using aug_max.
  :param int min_char: If word less than this value, do not draw word for augmentation
  :param list stopwords: List of words which will be skipped from augment operation.
  :param str stopwords_regex: Regular expression for matching words which will be skipped from augment operation.
  :param func tokenizer: Customize tokenization process
  :param func reverse_tokenizer: Customize reverse of tokenization process
  :param obj dict_of_path: Use pre-defined dictionary by default. Pass either file path of dict to use custom mapping. 
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.char as nac
  >>> aug = nac.OcrAug()
code: def __init__(
      self,
      name="OCR_Aug",
      aug_char_min=2,
      aug_char_max=10,
      aug_char_p=0.3,
      aug_word_p=0.3,
      aug_word_min=1,
      aug_word_max=10,
      stopwords=None,
      tokenizer=None,
      reverse_tokenizer=None,
      verbose=0,
      stopwords_regex=None,
      min_char=1,
      dict_of_path=None,
  ):
      super().__init__(
          action=Action.SUBSTITUTE,
          name=name,
          min_char=min_char,
          aug_char_min=aug_char_min,
          aug_char_max=aug_char_max,
          aug_char_p=aug_char_p,
          aug_word_min=aug_word_min,
          aug_word_max=aug_word_max,
          aug_word_p=aug_word_p,
          tokenizer=tokenizer,
          reverse_tokenizer=reverse_tokenizer,
          stopwords=stopwords,
          device="cpu",
          verbose=verbose,
          stopwords_regex=stopwords_regex,
          include_special_char=True,
          include_detail=False,
      )
  
      self.model = self.get_model(dict_of_path)
  
file_path: nlpaug/nlpaug/augmenter/char/ocr.py
imports: import os
  from nlpaug.augmenter.char import CharAugmenter
  from nlpaug.util import Action, Method, Doc, ReadUtil, LibraryUtil
  import nlpaug.model.char
label_desc: Use image node_id 1 to create a new OcrAug object from inherited base classes: CharAugmenter with example: obj = OcrAug(name, aug_char_min, aug_char_max, aug_char_p, aug_word_p, aug_word_min, aug_word_max, stopwords, tokenizer, reverse_tokenizer, verbose, stopwords_regex, min_char, dict_of_path)